{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization Tutorial 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and Setup the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import required library functions\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "#mpl.style.use('ggplot') # optional: for ggplot-like style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data, skip the top 20 and bottom 2 rows as they do not contain relevant data\n",
    "df_canada = pd.read_excel('data/canada.xlsx',\n",
    "                          sheet_name = 'Canada by Citizenship',\n",
    "                          skiprows = range(20),\n",
    "                          skipfooter = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conversion index and columns to lists\n",
    "df_canada.columns.tolist()\n",
    "df_canada.index.tolist()\n",
    "\n",
    "# remove unnecessary columns\n",
    "# in pandas axis=0 re|presents rows (default) and axis=1 represents columns.\n",
    "df_canada.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\n",
    "\n",
    "# rename some columns to make better sense\n",
    "df_canada.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert all column names to strings\n",
    "df_canada.columns = list(map(str, df_canada.columns))\n",
    "\n",
    "# full range of the time series\n",
    "years = list(map(str, range(1980, 2014)))\n",
    "\n",
    "# add Total column\n",
    "df_canada['Total'] = df_canada.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# index data by country\n",
    "df_canada.set_index('Country', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Parts of a Whole\n",
    "\n",
    "### Visualize continent wise immigration contributions into Canada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>340</td>\n",
       "      <td>496</td>\n",
       "      <td>...</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "      <td>58639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "      <td>15699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "      <td>69439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Continent           Region             DevName  1980  1981  \\\n",
       "Country                                                                     \n",
       "Afghanistan         Asia    Southern Asia  Developing regions    16    39   \n",
       "Albania           Europe  Southern Europe   Developed regions     1     0   \n",
       "Algeria           Africa  Northern Africa  Developing regions    80    67   \n",
       "American Samoa   Oceania        Polynesia  Developing regions     0     1   \n",
       "Andorra           Europe  Southern Europe   Developed regions     0     0   \n",
       "\n",
       "                1982  1983  1984  1985  1986  ...  2005  2006  2007  2008  \\\n",
       "Country                                       ...                           \n",
       "Afghanistan       39    47    71   340   496  ...  3436  3009  2652  2111   \n",
       "Albania            0     0     0     0     1  ...  1223   856   702   560   \n",
       "Algeria           71    69    63    44    69  ...  3626  4807  3623  4005   \n",
       "American Samoa     0     0     0     0     0  ...     0     1     0     0   \n",
       "Andorra            0     0     0     0     2  ...     0     1     1     0   \n",
       "\n",
       "                2009  2010  2011  2012  2013  Total  \n",
       "Country                                              \n",
       "Afghanistan     1746  1758  2203  2635  2004  58639  \n",
       "Albania          716   561   539   620   603  15699  \n",
       "Algeria         5393  4752  4325  3774  4331  69439  \n",
       "American Samoa     0     0     0     0     0      6  \n",
       "Andorra            0     0     0     1     1     15  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_canada.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
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       "      <th>1985</th>\n",
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       "      <th>2010</th>\n",
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       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>3951</td>\n",
       "      <td>4363</td>\n",
       "      <td>3819</td>\n",
       "      <td>2671</td>\n",
       "      <td>2639</td>\n",
       "      <td>2650</td>\n",
       "      <td>3782</td>\n",
       "      <td>7494</td>\n",
       "      <td>7552</td>\n",
       "      <td>9894</td>\n",
       "      <td>...</td>\n",
       "      <td>27523</td>\n",
       "      <td>29188</td>\n",
       "      <td>28284</td>\n",
       "      <td>29890</td>\n",
       "      <td>34534</td>\n",
       "      <td>40892</td>\n",
       "      <td>35441</td>\n",
       "      <td>38083</td>\n",
       "      <td>38543</td>\n",
       "      <td>618948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>31025</td>\n",
       "      <td>34314</td>\n",
       "      <td>30214</td>\n",
       "      <td>24696</td>\n",
       "      <td>27274</td>\n",
       "      <td>23850</td>\n",
       "      <td>28739</td>\n",
       "      <td>43203</td>\n",
       "      <td>47454</td>\n",
       "      <td>60256</td>\n",
       "      <td>...</td>\n",
       "      <td>159253</td>\n",
       "      <td>149054</td>\n",
       "      <td>133459</td>\n",
       "      <td>139894</td>\n",
       "      <td>141434</td>\n",
       "      <td>163845</td>\n",
       "      <td>146894</td>\n",
       "      <td>152218</td>\n",
       "      <td>155075</td>\n",
       "      <td>3317794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>39760</td>\n",
       "      <td>44802</td>\n",
       "      <td>42720</td>\n",
       "      <td>24638</td>\n",
       "      <td>22287</td>\n",
       "      <td>20844</td>\n",
       "      <td>24370</td>\n",
       "      <td>46698</td>\n",
       "      <td>54726</td>\n",
       "      <td>60893</td>\n",
       "      <td>...</td>\n",
       "      <td>35955</td>\n",
       "      <td>33053</td>\n",
       "      <td>33495</td>\n",
       "      <td>34692</td>\n",
       "      <td>35078</td>\n",
       "      <td>33425</td>\n",
       "      <td>26778</td>\n",
       "      <td>29177</td>\n",
       "      <td>28691</td>\n",
       "      <td>1410947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America and the Caribbean</th>\n",
       "      <td>13081</td>\n",
       "      <td>15215</td>\n",
       "      <td>16769</td>\n",
       "      <td>15427</td>\n",
       "      <td>13678</td>\n",
       "      <td>15171</td>\n",
       "      <td>21179</td>\n",
       "      <td>28471</td>\n",
       "      <td>21924</td>\n",
       "      <td>25060</td>\n",
       "      <td>...</td>\n",
       "      <td>24747</td>\n",
       "      <td>24676</td>\n",
       "      <td>26011</td>\n",
       "      <td>26547</td>\n",
       "      <td>26867</td>\n",
       "      <td>28818</td>\n",
       "      <td>27856</td>\n",
       "      <td>27173</td>\n",
       "      <td>24950</td>\n",
       "      <td>765148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern America</th>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>7705</td>\n",
       "      <td>6469</td>\n",
       "      <td>6790</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7677</td>\n",
       "      <td>7892</td>\n",
       "      <td>8503</td>\n",
       "      <td>241142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>1942</td>\n",
       "      <td>1839</td>\n",
       "      <td>1675</td>\n",
       "      <td>1018</td>\n",
       "      <td>878</td>\n",
       "      <td>920</td>\n",
       "      <td>904</td>\n",
       "      <td>1200</td>\n",
       "      <td>1181</td>\n",
       "      <td>1539</td>\n",
       "      <td>...</td>\n",
       "      <td>1585</td>\n",
       "      <td>1473</td>\n",
       "      <td>1693</td>\n",
       "      <td>1834</td>\n",
       "      <td>1860</td>\n",
       "      <td>1834</td>\n",
       "      <td>1548</td>\n",
       "      <td>1679</td>\n",
       "      <td>1775</td>\n",
       "      <td>55174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  1980   1981   1982   1983   1984   1985  \\\n",
       "Continent                                                                   \n",
       "Africa                            3951   4363   3819   2671   2639   2650   \n",
       "Asia                             31025  34314  30214  24696  27274  23850   \n",
       "Europe                           39760  44802  42720  24638  22287  20844   \n",
       "Latin America and the Caribbean  13081  15215  16769  15427  13678  15171   \n",
       "Northern America                  9378  10030   9074   7100   6661   6543   \n",
       "Oceania                           1942   1839   1675   1018    878    920   \n",
       "\n",
       "                                  1986   1987   1988   1989  ...    2005  \\\n",
       "Continent                                                    ...           \n",
       "Africa                            3782   7494   7552   9894  ...   27523   \n",
       "Asia                             28739  43203  47454  60256  ...  159253   \n",
       "Europe                           24370  46698  54726  60893  ...   35955   \n",
       "Latin America and the Caribbean  21179  28471  21924  25060  ...   24747   \n",
       "Northern America                  7074   7705   6469   6790  ...    8394   \n",
       "Oceania                            904   1200   1181   1539  ...    1585   \n",
       "\n",
       "                                   2006    2007    2008    2009    2010  \\\n",
       "Continent                                                                 \n",
       "Africa                            29188   28284   29890   34534   40892   \n",
       "Asia                             149054  133459  139894  141434  163845   \n",
       "Europe                            33053   33495   34692   35078   33425   \n",
       "Latin America and the Caribbean   24676   26011   26547   26867   28818   \n",
       "Northern America                   9613    9463   10190    8995    8142   \n",
       "Oceania                            1473    1693    1834    1860    1834   \n",
       "\n",
       "                                   2011    2012    2013    Total  \n",
       "Continent                                                         \n",
       "Africa                            35441   38083   38543   618948  \n",
       "Asia                             146894  152218  155075  3317794  \n",
       "Europe                            26778   29177   28691  1410947  \n",
       "Latin America and the Caribbean   27856   27173   24950   765148  \n",
       "Northern America                   7677    7892    8503   241142  \n",
       "Oceania                            1548    1679    1775    55174  \n",
       "\n",
       "[6 rows x 35 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by Continent\n",
    "df_continents = df_canada.groupby(['Continent'], axis = 0).sum()\n",
    "\n",
    "# show Continent wise distribution\n",
    "df_continents.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            labels=None,         # turn off labels on pie chart\n",
    "                            pctdistance=1.12,    # the ratio between the center of each pie slice and the start of the text generated by autopct \n",
    "                            )\n",
    "\n",
    "# scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]', y=1.12) \n",
    "\n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Categories and Sub-categories\n",
    "\n",
    "### Visualize total continent wise immigration into Canada between 1980 and 2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot immigration pattern from Continents\n",
    "df_continents['Total'].plot(kind = 'barh')\n",
    "plt.title('Immigration to Canada by Continent')\n",
    "plt.ylabel('Continents')\n",
    "plt.xlabel('Number of immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visually compare continent wise immigration into Canada from developed vs developing countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th>DevName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <th>Developing regions</th>\n",
       "      <td>3951</td>\n",
       "      <td>4363</td>\n",
       "      <td>3819</td>\n",
       "      <td>2671</td>\n",
       "      <td>2639</td>\n",
       "      <td>2650</td>\n",
       "      <td>3782</td>\n",
       "      <td>7494</td>\n",
       "      <td>7552</td>\n",
       "      <td>9894</td>\n",
       "      <td>...</td>\n",
       "      <td>27523</td>\n",
       "      <td>29188</td>\n",
       "      <td>28284</td>\n",
       "      <td>29890</td>\n",
       "      <td>34534</td>\n",
       "      <td>40892</td>\n",
       "      <td>35441</td>\n",
       "      <td>38083</td>\n",
       "      <td>38543</td>\n",
       "      <td>618948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Asia</th>\n",
       "      <th>Developed regions</th>\n",
       "      <td>701</td>\n",
       "      <td>756</td>\n",
       "      <td>598</td>\n",
       "      <td>309</td>\n",
       "      <td>246</td>\n",
       "      <td>198</td>\n",
       "      <td>248</td>\n",
       "      <td>422</td>\n",
       "      <td>324</td>\n",
       "      <td>494</td>\n",
       "      <td>...</td>\n",
       "      <td>1067</td>\n",
       "      <td>1212</td>\n",
       "      <td>1250</td>\n",
       "      <td>1284</td>\n",
       "      <td>1194</td>\n",
       "      <td>1168</td>\n",
       "      <td>1265</td>\n",
       "      <td>1214</td>\n",
       "      <td>982</td>\n",
       "      <td>27707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Developing regions</th>\n",
       "      <td>30324</td>\n",
       "      <td>33558</td>\n",
       "      <td>29616</td>\n",
       "      <td>24387</td>\n",
       "      <td>27028</td>\n",
       "      <td>23652</td>\n",
       "      <td>28491</td>\n",
       "      <td>42781</td>\n",
       "      <td>47130</td>\n",
       "      <td>59762</td>\n",
       "      <td>...</td>\n",
       "      <td>158186</td>\n",
       "      <td>147842</td>\n",
       "      <td>132209</td>\n",
       "      <td>138610</td>\n",
       "      <td>140240</td>\n",
       "      <td>162677</td>\n",
       "      <td>145629</td>\n",
       "      <td>151004</td>\n",
       "      <td>154093</td>\n",
       "      <td>3290087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <th>Developed regions</th>\n",
       "      <td>39760</td>\n",
       "      <td>44802</td>\n",
       "      <td>42720</td>\n",
       "      <td>24638</td>\n",
       "      <td>22287</td>\n",
       "      <td>20844</td>\n",
       "      <td>24370</td>\n",
       "      <td>46698</td>\n",
       "      <td>54726</td>\n",
       "      <td>60893</td>\n",
       "      <td>...</td>\n",
       "      <td>35955</td>\n",
       "      <td>33053</td>\n",
       "      <td>33495</td>\n",
       "      <td>34692</td>\n",
       "      <td>35078</td>\n",
       "      <td>33425</td>\n",
       "      <td>26778</td>\n",
       "      <td>29177</td>\n",
       "      <td>28691</td>\n",
       "      <td>1410947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America and the Caribbean</th>\n",
       "      <th>Developing regions</th>\n",
       "      <td>13081</td>\n",
       "      <td>15215</td>\n",
       "      <td>16769</td>\n",
       "      <td>15427</td>\n",
       "      <td>13678</td>\n",
       "      <td>15171</td>\n",
       "      <td>21179</td>\n",
       "      <td>28471</td>\n",
       "      <td>21924</td>\n",
       "      <td>25060</td>\n",
       "      <td>...</td>\n",
       "      <td>24747</td>\n",
       "      <td>24676</td>\n",
       "      <td>26011</td>\n",
       "      <td>26547</td>\n",
       "      <td>26867</td>\n",
       "      <td>28818</td>\n",
       "      <td>27856</td>\n",
       "      <td>27173</td>\n",
       "      <td>24950</td>\n",
       "      <td>765148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern America</th>\n",
       "      <th>Developed regions</th>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>7705</td>\n",
       "      <td>6469</td>\n",
       "      <td>6790</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7677</td>\n",
       "      <td>7892</td>\n",
       "      <td>8503</td>\n",
       "      <td>241142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oceania</th>\n",
       "      <th>Developed regions</th>\n",
       "      <td>1304</td>\n",
       "      <td>1119</td>\n",
       "      <td>848</td>\n",
       "      <td>457</td>\n",
       "      <td>481</td>\n",
       "      <td>467</td>\n",
       "      <td>532</td>\n",
       "      <td>675</td>\n",
       "      <td>610</td>\n",
       "      <td>790</td>\n",
       "      <td>...</td>\n",
       "      <td>1279</td>\n",
       "      <td>1193</td>\n",
       "      <td>1383</td>\n",
       "      <td>1498</td>\n",
       "      <td>1538</td>\n",
       "      <td>1423</td>\n",
       "      <td>1226</td>\n",
       "      <td>1399</td>\n",
       "      <td>1536</td>\n",
       "      <td>34215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Developing regions</th>\n",
       "      <td>638</td>\n",
       "      <td>720</td>\n",
       "      <td>827</td>\n",
       "      <td>561</td>\n",
       "      <td>397</td>\n",
       "      <td>453</td>\n",
       "      <td>372</td>\n",
       "      <td>525</td>\n",
       "      <td>571</td>\n",
       "      <td>749</td>\n",
       "      <td>...</td>\n",
       "      <td>306</td>\n",
       "      <td>280</td>\n",
       "      <td>310</td>\n",
       "      <td>336</td>\n",
       "      <td>322</td>\n",
       "      <td>411</td>\n",
       "      <td>322</td>\n",
       "      <td>280</td>\n",
       "      <td>239</td>\n",
       "      <td>20959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     1980   1981   1982  \\\n",
       "Continent                       DevName                                   \n",
       "Africa                          Developing regions   3951   4363   3819   \n",
       "Asia                            Developed regions     701    756    598   \n",
       "                                Developing regions  30324  33558  29616   \n",
       "Europe                          Developed regions   39760  44802  42720   \n",
       "Latin America and the Caribbean Developing regions  13081  15215  16769   \n",
       "Northern America                Developed regions    9378  10030   9074   \n",
       "Oceania                         Developed regions    1304   1119    848   \n",
       "                                Developing regions    638    720    827   \n",
       "\n",
       "                                                     1983   1984   1985  \\\n",
       "Continent                       DevName                                   \n",
       "Africa                          Developing regions   2671   2639   2650   \n",
       "Asia                            Developed regions     309    246    198   \n",
       "                                Developing regions  24387  27028  23652   \n",
       "Europe                          Developed regions   24638  22287  20844   \n",
       "Latin America and the Caribbean Developing regions  15427  13678  15171   \n",
       "Northern America                Developed regions    7100   6661   6543   \n",
       "Oceania                         Developed regions     457    481    467   \n",
       "                                Developing regions    561    397    453   \n",
       "\n",
       "                                                     1986   1987   1988  \\\n",
       "Continent                       DevName                                   \n",
       "Africa                          Developing regions   3782   7494   7552   \n",
       "Asia                            Developed regions     248    422    324   \n",
       "                                Developing regions  28491  42781  47130   \n",
       "Europe                          Developed regions   24370  46698  54726   \n",
       "Latin America and the Caribbean Developing regions  21179  28471  21924   \n",
       "Northern America                Developed regions    7074   7705   6469   \n",
       "Oceania                         Developed regions     532    675    610   \n",
       "                                Developing regions    372    525    571   \n",
       "\n",
       "                                                     1989  ...    2005  \\\n",
       "Continent                       DevName                    ...           \n",
       "Africa                          Developing regions   9894  ...   27523   \n",
       "Asia                            Developed regions     494  ...    1067   \n",
       "                                Developing regions  59762  ...  158186   \n",
       "Europe                          Developed regions   60893  ...   35955   \n",
       "Latin America and the Caribbean Developing regions  25060  ...   24747   \n",
       "Northern America                Developed regions    6790  ...    8394   \n",
       "Oceania                         Developed regions     790  ...    1279   \n",
       "                                Developing regions    749  ...     306   \n",
       "\n",
       "                                                      2006    2007    2008  \\\n",
       "Continent                       DevName                                      \n",
       "Africa                          Developing regions   29188   28284   29890   \n",
       "Asia                            Developed regions     1212    1250    1284   \n",
       "                                Developing regions  147842  132209  138610   \n",
       "Europe                          Developed regions    33053   33495   34692   \n",
       "Latin America and the Caribbean Developing regions   24676   26011   26547   \n",
       "Northern America                Developed regions     9613    9463   10190   \n",
       "Oceania                         Developed regions     1193    1383    1498   \n",
       "                                Developing regions     280     310     336   \n",
       "\n",
       "                                                      2009    2010    2011  \\\n",
       "Continent                       DevName                                      \n",
       "Africa                          Developing regions   34534   40892   35441   \n",
       "Asia                            Developed regions     1194    1168    1265   \n",
       "                                Developing regions  140240  162677  145629   \n",
       "Europe                          Developed regions    35078   33425   26778   \n",
       "Latin America and the Caribbean Developing regions   26867   28818   27856   \n",
       "Northern America                Developed regions     8995    8142    7677   \n",
       "Oceania                         Developed regions     1538    1423    1226   \n",
       "                                Developing regions     322     411     322   \n",
       "\n",
       "                                                      2012    2013    Total  \n",
       "Continent                       DevName                                      \n",
       "Africa                          Developing regions   38083   38543   618948  \n",
       "Asia                            Developed regions     1214     982    27707  \n",
       "                                Developing regions  151004  154093  3290087  \n",
       "Europe                          Developed regions    29177   28691  1410947  \n",
       "Latin America and the Caribbean Developing regions   27173   24950   765148  \n",
       "Northern America                Developed regions     7892    8503   241142  \n",
       "Oceania                         Developed regions     1399    1536    34215  \n",
       "                                Developing regions     280     239    20959  \n",
       "\n",
       "[8 rows x 35 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split data based on level of development\n",
    "df_development = df_canada.groupby(['Continent', 'DevName'], axis = 0).sum()\n",
    "\n",
    "df_development.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">1980</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1981</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1982</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1983</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1984</th>\n",
       "      <th>...</th>\n",
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       "      <th colspan=\"2\" halign=\"left\">2013</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DevName</th>\n",
       "      <th>Developed regions</th>\n",
       "      <th>Developing regions</th>\n",
       "      <th>Developed regions</th>\n",
       "      <th>Developing regions</th>\n",
       "      <th>Developed regions</th>\n",
       "      <th>Developing regions</th>\n",
       "      <th>Developed regions</th>\n",
       "      <th>Developing regions</th>\n",
       "      <th>Developed regions</th>\n",
       "      <th>Developing regions</th>\n",
       "      <th>...</th>\n",
       "      <th>Developed regions</th>\n",
       "      <th>Developing regions</th>\n",
       "      <th>Developed regions</th>\n",
       "      <th>Developing regions</th>\n",
       "      <th>Developed regions</th>\n",
       "      <th>Developing regions</th>\n",
       "      <th>Developed regions</th>\n",
       "      <th>Developing regions</th>\n",
       "      <th>Developed regions</th>\n",
       "      <th>Developing regions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3951.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4363.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3819.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2671.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2639.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40892.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35441.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38083.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38543.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>618948.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>701.0</td>\n",
       "      <td>30324.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>33558.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>29616.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>24387.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>27028.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1168.0</td>\n",
       "      <td>162677.0</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>145629.0</td>\n",
       "      <td>1214.0</td>\n",
       "      <td>151004.0</td>\n",
       "      <td>982.0</td>\n",
       "      <td>154093.0</td>\n",
       "      <td>27707.0</td>\n",
       "      <td>3290087.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>39760.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44802.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42720.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24638.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22287.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33425.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26778.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29177.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28691.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1410947.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America and the Caribbean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13081.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15215.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16769.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15427.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13678.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28818.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27856.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27173.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24950.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>765148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern America</th>\n",
       "      <td>9378.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10030.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9074.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6661.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7892.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8503.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>241142.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>1304.0</td>\n",
       "      <td>638.0</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>848.0</td>\n",
       "      <td>827.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1423.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>1226.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>1536.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>34215.0</td>\n",
       "      <td>20959.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             1980                     \\\n",
       "DevName                         Developed regions Developing regions   \n",
       "Continent                                                              \n",
       "Africa                                        0.0             3951.0   \n",
       "Asia                                        701.0            30324.0   \n",
       "Europe                                    39760.0                0.0   \n",
       "Latin America and the Caribbean               0.0            13081.0   \n",
       "Northern America                           9378.0                0.0   \n",
       "Oceania                                    1304.0              638.0   \n",
       "\n",
       "                                             1981                     \\\n",
       "DevName                         Developed regions Developing regions   \n",
       "Continent                                                              \n",
       "Africa                                        0.0             4363.0   \n",
       "Asia                                        756.0            33558.0   \n",
       "Europe                                    44802.0                0.0   \n",
       "Latin America and the Caribbean               0.0            15215.0   \n",
       "Northern America                          10030.0                0.0   \n",
       "Oceania                                    1119.0              720.0   \n",
       "\n",
       "                                             1982                     \\\n",
       "DevName                         Developed regions Developing regions   \n",
       "Continent                                                              \n",
       "Africa                                        0.0             3819.0   \n",
       "Asia                                        598.0            29616.0   \n",
       "Europe                                    42720.0                0.0   \n",
       "Latin America and the Caribbean               0.0            16769.0   \n",
       "Northern America                           9074.0                0.0   \n",
       "Oceania                                     848.0              827.0   \n",
       "\n",
       "                                             1983                     \\\n",
       "DevName                         Developed regions Developing regions   \n",
       "Continent                                                              \n",
       "Africa                                        0.0             2671.0   \n",
       "Asia                                        309.0            24387.0   \n",
       "Europe                                    24638.0                0.0   \n",
       "Latin America and the Caribbean               0.0            15427.0   \n",
       "Northern America                           7100.0                0.0   \n",
       "Oceania                                     457.0              561.0   \n",
       "\n",
       "                                             1984                     ...  \\\n",
       "DevName                         Developed regions Developing regions  ...   \n",
       "Continent                                                             ...   \n",
       "Africa                                        0.0             2639.0  ...   \n",
       "Asia                                        246.0            27028.0  ...   \n",
       "Europe                                    22287.0                0.0  ...   \n",
       "Latin America and the Caribbean               0.0            13678.0  ...   \n",
       "Northern America                           6661.0                0.0  ...   \n",
       "Oceania                                     481.0              397.0  ...   \n",
       "\n",
       "                                             2010                     \\\n",
       "DevName                         Developed regions Developing regions   \n",
       "Continent                                                              \n",
       "Africa                                        0.0            40892.0   \n",
       "Asia                                       1168.0           162677.0   \n",
       "Europe                                    33425.0                0.0   \n",
       "Latin America and the Caribbean               0.0            28818.0   \n",
       "Northern America                           8142.0                0.0   \n",
       "Oceania                                    1423.0              411.0   \n",
       "\n",
       "                                             2011                     \\\n",
       "DevName                         Developed regions Developing regions   \n",
       "Continent                                                              \n",
       "Africa                                        0.0            35441.0   \n",
       "Asia                                       1265.0           145629.0   \n",
       "Europe                                    26778.0                0.0   \n",
       "Latin America and the Caribbean               0.0            27856.0   \n",
       "Northern America                           7677.0                0.0   \n",
       "Oceania                                    1226.0              322.0   \n",
       "\n",
       "                                             2012                     \\\n",
       "DevName                         Developed regions Developing regions   \n",
       "Continent                                                              \n",
       "Africa                                        0.0            38083.0   \n",
       "Asia                                       1214.0           151004.0   \n",
       "Europe                                    29177.0                0.0   \n",
       "Latin America and the Caribbean               0.0            27173.0   \n",
       "Northern America                           7892.0                0.0   \n",
       "Oceania                                    1399.0              280.0   \n",
       "\n",
       "                                             2013                     \\\n",
       "DevName                         Developed regions Developing regions   \n",
       "Continent                                                              \n",
       "Africa                                        0.0            38543.0   \n",
       "Asia                                        982.0           154093.0   \n",
       "Europe                                    28691.0                0.0   \n",
       "Latin America and the Caribbean               0.0            24950.0   \n",
       "Northern America                           8503.0                0.0   \n",
       "Oceania                                    1536.0              239.0   \n",
       "\n",
       "                                            Total                     \n",
       "DevName                         Developed regions Developing regions  \n",
       "Continent                                                             \n",
       "Africa                                        0.0           618948.0  \n",
       "Asia                                      27707.0          3290087.0  \n",
       "Europe                                  1410947.0                0.0  \n",
       "Latin America and the Caribbean               0.0           765148.0  \n",
       "Northern America                         241142.0                0.0  \n",
       "Oceania                                   34215.0            20959.0  \n",
       "\n",
       "[6 rows x 70 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_development = df_development.unstack('DevName').fillna(0)\n",
    "\n",
    "df_development.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot immigration pattern from continents based on level of development\n",
    "df_development['Total'].plot(kind = 'barh', stacked=True)\n",
    "\n",
    "plt.title('Immigration to Canada by Continent and Development Level')\n",
    "plt.ylabel('Continents')\n",
    "plt.xlabel('Number of immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Proportions varying over Time\n",
    "\n",
    "### Visualize total immigration contributions from each continent over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_continents[years].transpose().plot(kind='area', stacked=True)\n",
    "\n",
    "plt.title('Immigration to Canada by Continent over time')\n",
    "plt.ylabel('Continents')\n",
    "plt.xlabel('Number of immigrants')\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize fractional immigration contributions from each continent over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>0.039854</td>\n",
       "      <td>0.039462</td>\n",
       "      <td>0.036626</td>\n",
       "      <td>0.035354</td>\n",
       "      <td>0.035945</td>\n",
       "      <td>0.037869</td>\n",
       "      <td>0.043952</td>\n",
       "      <td>0.055605</td>\n",
       "      <td>0.054212</td>\n",
       "      <td>0.060171</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106903</td>\n",
       "      <td>0.118143</td>\n",
       "      <td>0.121701</td>\n",
       "      <td>0.122980</td>\n",
       "      <td>0.138820</td>\n",
       "      <td>0.147648</td>\n",
       "      <td>0.143956</td>\n",
       "      <td>0.148633</td>\n",
       "      <td>0.149660</td>\n",
       "      <td>0.096573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>0.312951</td>\n",
       "      <td>0.310357</td>\n",
       "      <td>0.289764</td>\n",
       "      <td>0.326883</td>\n",
       "      <td>0.371494</td>\n",
       "      <td>0.340821</td>\n",
       "      <td>0.333988</td>\n",
       "      <td>0.320566</td>\n",
       "      <td>0.340646</td>\n",
       "      <td>0.366449</td>\n",
       "      <td>...</td>\n",
       "      <td>0.618562</td>\n",
       "      <td>0.603318</td>\n",
       "      <td>0.574252</td>\n",
       "      <td>0.575584</td>\n",
       "      <td>0.568538</td>\n",
       "      <td>0.591592</td>\n",
       "      <td>0.596660</td>\n",
       "      <td>0.594086</td>\n",
       "      <td>0.602146</td>\n",
       "      <td>0.517665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>0.401061</td>\n",
       "      <td>0.405217</td>\n",
       "      <td>0.409702</td>\n",
       "      <td>0.326115</td>\n",
       "      <td>0.303567</td>\n",
       "      <td>0.297865</td>\n",
       "      <td>0.283214</td>\n",
       "      <td>0.346499</td>\n",
       "      <td>0.392847</td>\n",
       "      <td>0.370323</td>\n",
       "      <td>...</td>\n",
       "      <td>0.139654</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>0.144123</td>\n",
       "      <td>0.142738</td>\n",
       "      <td>0.141007</td>\n",
       "      <td>0.120687</td>\n",
       "      <td>0.108768</td>\n",
       "      <td>0.113874</td>\n",
       "      <td>0.111405</td>\n",
       "      <td>0.220146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America and the Caribbean</th>\n",
       "      <td>0.131949</td>\n",
       "      <td>0.137614</td>\n",
       "      <td>0.160821</td>\n",
       "      <td>0.204196</td>\n",
       "      <td>0.186306</td>\n",
       "      <td>0.216797</td>\n",
       "      <td>0.246130</td>\n",
       "      <td>0.211255</td>\n",
       "      <td>0.157380</td>\n",
       "      <td>0.152403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096121</td>\n",
       "      <td>0.099880</td>\n",
       "      <td>0.111921</td>\n",
       "      <td>0.109226</td>\n",
       "      <td>0.108000</td>\n",
       "      <td>0.104053</td>\n",
       "      <td>0.113147</td>\n",
       "      <td>0.106053</td>\n",
       "      <td>0.096879</td>\n",
       "      <td>0.119384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern America</th>\n",
       "      <td>0.094596</td>\n",
       "      <td>0.090718</td>\n",
       "      <td>0.087023</td>\n",
       "      <td>0.093977</td>\n",
       "      <td>0.090728</td>\n",
       "      <td>0.093501</td>\n",
       "      <td>0.082210</td>\n",
       "      <td>0.057171</td>\n",
       "      <td>0.046437</td>\n",
       "      <td>0.041294</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032604</td>\n",
       "      <td>0.038910</td>\n",
       "      <td>0.040718</td>\n",
       "      <td>0.041926</td>\n",
       "      <td>0.036158</td>\n",
       "      <td>0.029398</td>\n",
       "      <td>0.031183</td>\n",
       "      <td>0.030801</td>\n",
       "      <td>0.033017</td>\n",
       "      <td>0.037625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     1980      1981      1982      1983  \\\n",
       "Continent                                                                 \n",
       "Africa                           0.039854  0.039462  0.036626  0.035354   \n",
       "Asia                             0.312951  0.310357  0.289764  0.326883   \n",
       "Europe                           0.401061  0.405217  0.409702  0.326115   \n",
       "Latin America and the Caribbean  0.131949  0.137614  0.160821  0.204196   \n",
       "Northern America                 0.094596  0.090718  0.087023  0.093977   \n",
       "\n",
       "                                     1984      1985      1986      1987  \\\n",
       "Continent                                                                 \n",
       "Africa                           0.035945  0.037869  0.043952  0.055605   \n",
       "Asia                             0.371494  0.340821  0.333988  0.320566   \n",
       "Europe                           0.303567  0.297865  0.283214  0.346499   \n",
       "Latin America and the Caribbean  0.186306  0.216797  0.246130  0.211255   \n",
       "Northern America                 0.090728  0.093501  0.082210  0.057171   \n",
       "\n",
       "                                     1988      1989  ...      2005      2006  \\\n",
       "Continent                                            ...                       \n",
       "Africa                           0.054212  0.060171  ...  0.106903  0.118143   \n",
       "Asia                             0.340646  0.366449  ...  0.618562  0.603318   \n",
       "Europe                           0.392847  0.370323  ...  0.139654  0.133787   \n",
       "Latin America and the Caribbean  0.157380  0.152403  ...  0.096121  0.099880   \n",
       "Northern America                 0.046437  0.041294  ...  0.032604  0.038910   \n",
       "\n",
       "                                     2007      2008      2009      2010  \\\n",
       "Continent                                                                 \n",
       "Africa                           0.121701  0.122980  0.138820  0.147648   \n",
       "Asia                             0.574252  0.575584  0.568538  0.591592   \n",
       "Europe                           0.144123  0.142738  0.141007  0.120687   \n",
       "Latin America and the Caribbean  0.111921  0.109226  0.108000  0.104053   \n",
       "Northern America                 0.040718  0.041926  0.036158  0.029398   \n",
       "\n",
       "                                     2011      2012      2013     Total  \n",
       "Continent                                                                \n",
       "Africa                           0.143956  0.148633  0.149660  0.096573  \n",
       "Asia                             0.596660  0.594086  0.602146  0.517665  \n",
       "Europe                           0.108768  0.113874  0.111405  0.220146  \n",
       "Latin America and the Caribbean  0.113147  0.106053  0.096879  0.119384  \n",
       "Northern America                 0.031183  0.030801  0.033017  0.037625  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute Continent wise proportion\n",
    "df_fraction = df_continents.divide(df_continents.sum(axis = 0), axis = 1)\n",
    "\n",
    "df_fraction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_fraction[years].transpose().plot(kind='area', stacked=True)\n",
    "\n",
    "plt.title('Immigration to Canada by Continent over time')\n",
    "plt.ylabel('Continents')\n",
    "plt.xlabel('Number of immigrants')\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Parts of a Whole using Waffle Charts\n",
    "\n",
    "### Visualize continent wise immigration contributions into Canada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# special library for Waffle Charts\n",
    "from pywaffle import Waffle\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Continent\n",
       "Africa                              9.657251\n",
       "Asia                               51.766497\n",
       "Europe                             22.014563\n",
       "Latin America and the Caribbean    11.938364\n",
       "Northern America                    3.762463\n",
       "Oceania                             0.860863\n",
       "Name: Total, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by Continent\n",
    "df_continents = df_canada.groupby(['Continent'], axis = 0).sum()\n",
    "\n",
    "# compute fraction\n",
    "df_fraction = df_continents.divide(df_continents.sum(axis = 0), axis = 1)['Total'] * 100\n",
    "\n",
    "df_fraction.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Continent\n",
       "Africa                             10\n",
       "Asia                               52\n",
       "Europe                             22\n",
       "Latin America and the Caribbean    12\n",
       "Northern America                    4\n",
       "Oceania                             1\n",
       "Name: Total, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_waffle = df_fraction.round(0).astype(int)\n",
    "\n",
    "df_waffle.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Waffle size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot waffle chart\n",
    "data = json.loads(df_waffle.to_json(orient='index'))\n",
    "fig = plt.figure(\n",
    "    FigureClass=Waffle,\n",
    "    rows=5, \n",
    "    values=data,\n",
    "    figsize=(10, 4),\n",
    "    title={'label': 'Immigration to Canada by Continent [1980 - 2013]', 'loc': 'center'},\n",
    "    legend={'loc': 'upper left', 'bbox_to_anchor': (0.0, 0.0), 'ncol': len(data), 'framealpha': 0}\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Africa': 10,\n",
       " 'Asia': 52,\n",
       " 'Europe': 22,\n",
       " 'Latin America and the Caribbean': 12,\n",
       " 'Northern America': 4,\n",
       " 'Oceania': 1}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
